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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2509.13720 |
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| _version_ | 1866908544191692800 |
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| author | Zeng, Tianle Peng, Jianwei Ye, Hanjing Chen, Guangcheng Luo, Senzi Zhang, Hong |
| author_facet | Zeng, Tianle Peng, Jianwei Ye, Hanjing Chen, Guangcheng Luo, Senzi Zhang, Hong |
| contents | Zero-shot object navigation (ZSON) in large-scale outdoor environments faces many challenges; we specifically address a coupled one: long-range targets that reduce to tiny projections and intermittent visibility due to partial or complete occlusion. We present a unified, lightweight closed-loop system built on an aligned multi-scale image tile hierarchy. Through hierarchical target-saliency fusion, it summarizes localized semantic contrast into a stable coarse-layer regional saliency that provides the target direction and indicates target visibility. This regional saliency supports visibility-aware heading maintenance through keyframe memory, saliency-weighted fusion of historical headings, and active search during temporary invisibility. The system avoids whole-image rescaling, enables deterministic bottom-up aggregation, supports zero-shot navigation, and runs efficiently on a mobile robot. Across simulation and real-world outdoor trials, the system detects semantic targets beyond 150m, maintains a correct heading through visibility changes with 82.6% probability, and improves overall task success by 17.5% compared with the SOTA methods, demonstrating robust ZSON toward distant and intermittently observable targets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13720 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EZREAL: Enhancing Zero-Shot Outdoor Robot Navigation toward Distant Targets under Varying Visibility Zeng, Tianle Peng, Jianwei Ye, Hanjing Chen, Guangcheng Luo, Senzi Zhang, Hong Robotics Zero-shot object navigation (ZSON) in large-scale outdoor environments faces many challenges; we specifically address a coupled one: long-range targets that reduce to tiny projections and intermittent visibility due to partial or complete occlusion. We present a unified, lightweight closed-loop system built on an aligned multi-scale image tile hierarchy. Through hierarchical target-saliency fusion, it summarizes localized semantic contrast into a stable coarse-layer regional saliency that provides the target direction and indicates target visibility. This regional saliency supports visibility-aware heading maintenance through keyframe memory, saliency-weighted fusion of historical headings, and active search during temporary invisibility. The system avoids whole-image rescaling, enables deterministic bottom-up aggregation, supports zero-shot navigation, and runs efficiently on a mobile robot. Across simulation and real-world outdoor trials, the system detects semantic targets beyond 150m, maintains a correct heading through visibility changes with 82.6% probability, and improves overall task success by 17.5% compared with the SOTA methods, demonstrating robust ZSON toward distant and intermittently observable targets. |
| title | EZREAL: Enhancing Zero-Shot Outdoor Robot Navigation toward Distant Targets under Varying Visibility |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.13720 |